How_To_Drive_Analytics_Adoption_via_Data_Transformation

How to Drive Analytics Adoption via Data Transformation

Have you spent long evenings and weekends building a modern data infrastructure and still struggling to get the rest of the organization to adopt it? Here is a sure way to get wider business adoption by getting the business teams involved.

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About the How To Drive Analytics Adoption via Data Transformation White Paper

Data transformation has become an integral component to every modern data stack, alongside your cloud data warehouse such as Snowflake.  Data transformation also plays an indispensable role in the overall data lifecycle and analytics process, turning raw source data into valuable analytics data assets.

DataOps Process: How it helps

Data Transformation Workflow

The movement to cloud analytics and data warehouses has altered the overall data lifecycle and data transformation workflow

DataOps Process: Drivers and Objectives of DataOps

The Role of Collaboration

Collaboration is required to make the process extremely efficient and ensure the team is highly productive

DataOps Process: Data Platform Capabilities

Multi-persona Tools and Interfaces

The different personas involved in the data transformation workflow will have varying skills, technical expertise, and business knowledge

Search and Discovery

Rich faceted search capability, allowing users to easily search and drill down to potential assets they could use

SHARED WORKSPACES

Data transformation collaboration begins with teams working together in a common workspace to create the best models for their analytics.  Data teams will know a lot about the data, while analysts and data scientists will know a good deal about how the business will use the data and how the final form needs to look.  In shared workspaces, they can collaborate, share their knowledge, and share the modeling workload.

 

DATA DOCUMENTATION

Information about data is often sparse or non-existent.  The information is usually spread among wiki pages, metadata management systems, or early versions of data catalogs.  Most of these sources still do not capture much of the knowledge there is about the data.  Some data transformation tools attempt to generate documentation about data, but often this is just taking comments from SQL code and generating a wiki page or adding a limited description.

Datameer DTaaS facilitates capturing as much information as possible about the data it is working with, the transformations performed, and the resulting data models.

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